Target Tracking for Robot-Fishes Based on Unscented Kalman Filter

2014 ◽  
Vol 536-537 ◽  
pp. 979-983
Author(s):  
Peng Fei Bai ◽  
Yu Lian Jiang ◽  
Rong Fang Lei
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Water Waves ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Tracking Error ◽  
Sampling Strategy ◽  
Underwater Robot ◽  
Fast Tracking ◽  
Robot Fish

Because of the nonlinear motion of the robot-fish and the uncertain water waves, it is difficult to track dynamic target accurately and quickly for the robot-fish. To solve this problem, this paper proposed the target tracking strategy of underwater robot-fish based on Unscented Kalman filter (UKF). UKF is a nonlinear filtering on the basis of unscented transformation, which consists of prediction and update recursively, thus estimate the state of the system. To avoid the particle degeneracy, this paper selects sigma points adopting scale symmetric sampling strategy. We apply UKF method to the underwater robot-fish competition in China. Simulations show that the Robot-fish has fast tracking speed, shorter tracking path and smaller tracking error using UKF method than not using the method. It has excellent tracking performance.

Radar Target Tracking Error Research Using Kalman Filter-Based Algorithms

2012 ◽  
Vol 239-240 ◽  
pp. 1184-1187
Author(s):  
Qian Long Chai ◽  
Yu Long Bai ◽  
Cun Hui Dong
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Tracking Error ◽  
Substantial Effect ◽  
Specific Model ◽  
Radar Target ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Radar Systems

The methods of radar target tracking have a substantial effect on the accuracy of the whole radar systems. The basic principles and implementing steps of the Extended Kalman filter (the EKF) and the Unscented Kalman filter (the UKF) are briefly introduced. The main sources of radar observation errors and the limitation of the current methods are analyzed. According to the requirements of tracking a CV target, the EKF and the UKF are used to simulate the experiments by establishing the specific model of radar target tracking. The results show that the tracking errors can be constrained within a certain range and the whole systems also have the high tracking accuracy.

scholarly journals Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1088 ◽  
Author(s):  
Wasiq Ali ◽  
Yaan Li ◽  
Zhe Chen ◽  
Muhammad Asif Zahoor Raja ◽  
Nauman Ahmed ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
State Space Model ◽  
Moving Target ◽  
Bayesian Filtering ◽  
Tracking Problem ◽  
Cubature Kalman Filter ◽  
Passive Target Tracking

In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate state estimation of a far-field moving target in complex ocean environments. The nonlinear model of a Kalman filter based on a Spherical Radial Cubature Kalman Filter (SRCKF) and discrete-time Kalman smoother known as a Spherical Radial Cubature Rauch–Tung–Striebel (SRCRTS) smoother are applied for tracking the semi-curved and curved trajectory of a moving object. The worth of spherical radial cubature Bayesian filtering and smoothing algorithms is validated by comparing with a conventional Unscented Kalman Filter (UKF) and an Unscented Rauch–Tung–Striebel (URTS) smoother. Performance analysis of these techniques is performed for white Gaussian measured noise variations, which is a significant factor in passive target tracking, while the Bearings Only Tracking (BOT) technology is used for modeling of a passive target tracking framework. Simulations based experiments are executed for obtaining least Root Mean Square Error (RMSE) among a true and estimated position of a moving target at every time instant in Cartesian coordinates. Numerical results endorsed the validation of SRCKF and SRCRTS smoothers with better convergence and accuracy rates than that of UKF and URTS for each scenario of passive target tracking problem.

Spherical Simplex Unscented Kalman Filter-Based Jumping and Static Interacting Multiple Model

2017 ◽  
Vol 32 (04) ◽  
pp. 1850010
Author(s):  
Yi Pan ◽  
Hui Ye ◽  
Keke He
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
System Parameter ◽  
Unscented Kalman Filter ◽  
Continuous System ◽  
The State ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Real Time System ◽  
Spherical Simplex

A modified interacting multiple model (IMM) method called spherical simplex unscented Kalman filter-based jumping and static IMM (SSUKF-JSIMM) is proposed to solve the problem of nonlinear filtering with unknown continuous system parameter. SSUKF-JSIMM regards the continuous system parameter space as a union of disjoint regions, and each region is assigned to a model. For each model, under the assumption that the parameter belongs to the corresponding region, one sub-filter is used to estimate the parameter and the state when the parameter is presumed to be jumping, and another sub-filter is used to estimate the parameter and the state when the parameter is presumed to be static. Considering that spherical simplex unscented Kalman filter (SSUKF) is more suitable for a real-time system than the unscented Kalman filter (UKF), SSUKFs are adopted as the sub-filters of SSUKF-JSIMM. Results of the two SSUKFs are fused as the estimation output of the model. Experimental results show that SSUKF-JSIMM achieves higher performance than IMM, SIR, and UKF in bearings-only tracking problem.

scholarly journals A New Linear Regression Kalman Filter with Symmetric Samples

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2139
Author(s):  
Xiuqiong Chen ◽  
Jiayi Kang ◽  
Mina Teicher ◽  
Stephen S.-T. Yau
Keyword(s):  
Kalman Filter ◽  
Linear Regression ◽  
Particle Filter ◽  
Normal Distribution ◽  
Extended Kalman Filter ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Standard Normal Distribution ◽  
Leibler Divergence ◽  
Standard Normal

Nonlinear filtering is of great significance in industries. In this work, we develop a new linear regression Kalman filter for discrete nonlinear filtering problems. Under the framework of linear regression Kalman filter, the key step is minimizing the Kullback–Leibler divergence between standard normal distribution and its Dirac mixture approximation formed by symmetric samples so that we can obtain a set of samples which can capture the information of reference density. The samples representing the conditional densities evolve in a deterministic way, and therefore we need less samples compared with particle filter, as there is less variance in our method. The numerical results show that the new algorithm is more efficient compared with the widely used extended Kalman filter, unscented Kalman filter and particle filter.

scholarly journals Adaptive fading factor unscented Kalman filter with application to target tracking

2020 ◽  
Author(s):  
Peng Gu ◽  
Zhongliang Jing ◽  
Liangbin Wu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
System Model ◽  
Mismatch Error ◽  
Tracking Accuracy ◽  
Model Mismatch ◽  
Error Covariance ◽  
Nonlinear Tracking ◽  
Fading Factor

AbstractOne purpose of target tracking is to estimate the states of targets, and unscented Kalman filter is one of the effective algorithms for estimating in the nonlinear tracking problem. Considering the characteristics of complex maneuverability, it is easy to reduce the tracking accuracy and cause divergence due to the mismatch between the system model and the practical target motion model. Adaptive fading factor is an effective counter to this problem, having been instrumental in solving accuracy and divergence problems. Fading factor can adaptively adjust covariance matrix online to compensate model mismatch error. Moreover, fading factor not only improves the filtering accuracy, but also automatically adjusts the error covariance in response to the different situation. The simulation results show that the adaptive fading factor unscented Kalman filter has more advantages in target tracking and it can be better applied to nonlinear target tracking.

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